A spatial entity is abstraction for an entity or phenomenon that exists or is virtualized in the natural world, correlates with a spatial position or feature, and is the minimum unit that can not be divided in the natural world. There are four types of basic spatial entities, i.e., point, line, surface and space. Spatial data is used for representing the spatial position, form information and spatial relation of the spatial entity itself, such as the information of the topology relation. The spatial data structure of the spatial data includes a vector data structure and a raster data structure. The vector data structure describes a spatial entity by a spatial discrete point coordinate, and views the entire studied space as a spatial domain, and the spatial entity is distributed in this spatial domain as an independent object. The raster data structure divides the space into uniform grids to describe a spatial entity with the characteristic of continuous distribution in a specific space.
With rapid development of spatial information technology, the obtained spatial data with high resolution and high accuracy increases explosively, however it gives rise to a series of problems, of which the most significant one is the real-time rapid transmission and display of the vector data in the massive spatial data of a high resolution map, one of the key methods for solving this problem is to simply the vector data before being transmitted and displayed. Typically, the existing method for simplifying vector data is Douglas-Peucker method, the basic idea of which is: connecting the head point and the end point of a curve virtually, then calculating the distance from all the points to this straight line and finding the maximum distance value dmax, and comparing the dmax with a tolerance D, discarding all the middle points on this curve if dmax<D; and remaining the coordinate point corresponding to the dmax, dividing the curve into two parts by taking this point as boundary, and applying this method to those two parts, if dmax≧D. This method has the following disadvantages: 1. the distance valve D is generally selected experientially based on the complexity of the vector data judged artificially, thus the threshold of the distance based on the human experience determines the number of the remained point after the vector data is simplified; 2. the most significant default of this method is that the spatial relation between the vector data is not considered, and it can not be ensured that all spatial relations between the simplified vector data are displayed correctly; 3. the lossless display simplification can not be performed according to the amplification ratio of the vector which is displayed on the client, i.e., the self-adaptive simplification can not be performed; and 4. the amount of the calculation is huge, the efficiency is low, and it is difficult to simplify massive vector data in real time.